Session ONE: Big Data and Disruptive Innovation
- Exploring the latest trends in the big data space
- Impact of data on business models and profitability
- Disruptive technologies like AI, Machine Learning, IoT and their application to solving business problems
- Leading large scale data-driven transformation across your enterprise
- Achieving ROI from your data projects
- Moving to real-time analysis for better responsiveness and forecasting
- Security, compliance, privacy and transparent use of data
- Turning data into new visibility and business intelligence
- Utilising predictive analytics for impactful action
- Widening data applications to achieve specific business outcomes – from personalised customer journeys to marketing and more
Conference Chair’s Opening Address
Opening Keynote Address: Why Strong Data-driven Leadership Matters
Big data technologies are transforming traditional enterprise structures and practices. As the modern business environment grows more sophisticated, we ask what is the role of leaders in driving forward data-driven cultures? In this opening session, we explore:
- Successful applications of big data technologies: tactics to lead with fit-for-purpose but flexible strategies for an evolving landscape
- Organising and structure a team, improving business-case creation, creative problem solving
- Ensuring cross-collaboration between data scientists and business executives
- Broadening understanding around ethical and data protection imperatives
Trust Issues? Building Ethical Data Stewardship
Data processing is highly regulated via a variety of different jurisdictions across the entire globe. The EU’s GDPR has set high standards, aiming for a more trustworthy data sharing environment. Yet there still remains a trust deficit around data acquisition, processing, and analysing despite strict regulations.
In this session, we explore:
- What is trustworthy and ethical data stewardship? A working definition
- How to build governance and stewardship within your enterprise
- Data stewardship best practices, using stewardship maturity models to benchmark compliance
Engaging Business Leaders With Data Storytelling
Do you want to tell a better story with your data? Would you like your data visualisation to lead to a better understanding of your business and provide some actionable insights? In this session, we consider how data visualisation can be a valuable social currency in your enterprise, allowing business functions to share insights and make new discoveries. We discuss:
- When to use data visualisation
- Understanding context and target audience
- Choosing effective visualisation tools
- Essential criteria of a compelling story
How an AI Centre of Excellence Can Help Your Enterprise
The idea of establishing a specialised department dedicated to AI is not especially new. It is becoming increasingly popular among the top-line enterprises. Around 40% of large organisations using AI have established centralised AI oversight groups. We consider:
- How to organise an AI team, what their competencies are, what type of collaborations you should look for, and how to arrange the AI infrastructure.
- Tips for creating clear objectives and priorities to develop AI capabilities
- Leveraging MLOps and DataOps to Operationalize ML and AI
Creating A Data-Driven Culture
Siloed traditional models, inability to understand the immense amount of data, borderline Data IQ offices are growing issues. There is an increasing need for data and analytics leaders to follow the example of English as a second language and treat information as the new second language for business.
- Data Dexterity – Identifying language gaps and establishing an ISL proof of concept for language development
- How can leaders (e.g. CDO) become the boosters of curiosity and critical thinking in the workforce
- How to create office spaces that take advantage of the potential of a data-driven workplace
- Making data accessible across the enterprise, integrating your data across siloed functions
- Establishing connections between your data and business objectives
- Using data to help make informed decisions
Questions To The Panel Of Speakers
Refreshment Break Served in the Exhibition Area
On-Premises Vs Cloud for Data Infrastructure
Cloud computing has gained popularity due to the time and money-saving improvements it offers, and it is here to stay. Cloud as a platform for databases is also growing at high-speed thanks to a vast range of cloud hosting services and dbPaaS. In this session, we consider:
- Why and how to move your data infrastructure to the Cloud: weighing the risks and benefits
- Different cloud platforms for your databases, how to choose the right vendor
- The TCO of cloud data infrastructure
Selecting the Core of your Data and Analytics Platform
Data is growing at a fast pace, and so have the number of storage options. Data lakes, Data hubs and Data warehouse have similar core functions, and they are often mistakenly understood as interchangeable. The reality is that they usually store different types of data, have different data standards and use diverse data systems. This is why it is essential to pick the right core for your enterprise. We discuss:
- Understanding the differences between hubs, lakes and warehouses
- Assessing what fits best for the data you want to store (e.g. flexibility, semantic enablement, size)
- What are the technology options for each core platform, and how can you integrate them?
Questions to the Panel of Speakers and Delegates move to the Seminar Rooms
(To view topics see the seminars page)
Networking Lunch Served in the Exhibition Area
Session TWO: Making Data the Centrepiece of your Business
- Exploring common pitfalls and how to avoid these
- Solving Critical Challenges and Fulfilling your Strategic Vision
- Cultivating a data-driven culture, people, and skills
- Managing and implementing a secure and scalable Big Data architecture
Conference Chair’s Afternoon Address
Case Study – Discovering Quants: The Wolf Data Scientists of Wall Street
The need for quantitative finance expertise is increasingly growing, and it is clear that the role of the “quant” – quantitative analysts – has changed significantly. New tasks need new skills. Alternative data, crypto, AI and blockchain have all opened whole new avenues for quantitative analytics to expand.
We explore the challenges/strategies that “quants” face from applying AI and machine learning to a variety of quantitative finance issues (e.g. risk management, trading) to the use of alternative data for forecasting purposes.
Case Study – Unleashing the Power of Customer Analytics
Customer analytics is one of the principal drivers of big data analytics adoption. Yet, the sheer variety of potential opportunities and applications to deliver excellent customer experience is overwhelming. We look at:
- Key trends and best practices in customer analytics
- Personalisation and customer journey analytics
- How partnering with service providers can help you mature your customer analytics
- Adapting tools and strategies to fit your business (e.g. supply chain and merchandising applications, chatbots, CDP)
- Whether Customer 360 view is only a utopian panacea
Case Study – Rapidly Maturing Big Data Analytics in the Healthcare industry
The analysis of a large amount of information has taken hold across the globe and has impacted the Life Sciences sector as well. Patient records, insurance information, and more, have become an essential part of the core of the healthcare industry, providing highly valuable information needed to offer lifesaving diagnoses or treatment options.
We discuss real case studies in which Big Data has empowered Life Sciences enterprises, from risk scoring for chronic diseases to discovering new therapies and improving patient engagement.
Questions to the Panel of Speakers
Afternoon Networking and Refreshments served in the Exhibition Area
Deep Learning: Unleash Your Data by Creating A Neural Network
Enterprises’ need to obtain more insights from their data, the upsurge in more powerful hardware and the explosion of Big Data, have led to the development of deep learning. We discuss:
- Understanding Deep learning. What is it? What can I do with it?
- Exploring diverse applications: extracting complex patterns, semantic indexing, data tagging, simplifying discriminative tasks and more
- How to overcome deep learning challenges (e.g. model optimisations, expensive investments)
Augmented Analytics: I have data,now what?
Augmented analytics has emerged as a potential solution for this widespread problem of turning vast troves of data into meaningful insights. We explore:
- What is augmented analytics, and why should I invest in it?
- What are the roadblocks? Assessing technology challenges, investment risks and lack of skills.
- Exploring early successful stories (e.g. the medical industry training programmes)
- Application strategies: extracting complex patterns, semantic indexing, data tagging, simplifying discriminative tasks and more
2020. Have We Achieved What Was Predicted?
2020 is here. A plethora of predictions was written regarding Big Data for this year. Have we met any of the expectations? Were the predictors, right? Has IoT finally integrated with Big Data? Have automated analytics finally changed the way we read data? Has GDPR forced companies to discard stored Data? Has a business with data-driven approaches won as much as it was predicted?
We explore the current landscape and forecast what the next decade may hold.
Do you want to place your bet?
Questions to the Panel of Speakers
Closing Remarks from the Conference Chair
Whitehall Media reserve the right to change the programme without prior notice.